* Add concat_prev layer, and U-net example for semantic segmentation
* Allow to supply mini-batch size as command-line parameter
* Decrease default mini-batch size from 30 to 24
* Resize t1, if needed
* Use DenseNet-style blocks instead of residual learning
* Increase default mini-batch size to 50
* Increase default mini-batch size from 50 to 60
* Resize even during the backward step, if needed
* Use resize_bilinear_gradient for the backward step
* Fix function call ambiguity problem
* Clear destination before adding gradient
* Works OK-ish
* Add more U-tags
* Tweak default mini-batch size
* Define a simpler network when using Microsoft Visual C++ compiler; clean up the DenseNet stuff (leaving it for a later PR)
* Decrease default mini-batch size from 24 to 23
* Define separate dnn filename for MSVC++ and not
* Add documentation for the resize_to_prev layer; move the implementation so that it comes after mult_prev
* Fix previous typo
* Minor formatting changes
* Reverse the ordering of levels
* Increase the learning-rate stopping criterion back to 1e-4 (was 1e-8)
* Use more U-tags even on Windows
* Minor formatting
* Latest MSVC 2017 builds fast, so there's no need to limit the depth any longer
* Tweak default mini-batch size again
* Even though latest MSVC can now build the extra layers, it does not mean we should add them!
* Fix naming
* Problem: integer overflow when calculating sizes (may happen e.g. with very large images)
Solution: change some types from (unsigned) long to size_t
# Conflicts:
# dlib/dnn/tensor.h
* Fix the fact that std::numeric_limits<unsigned long>::max() isn't always the same number
* Revert serialization changes
* Review fix: use long long instead of size_t
* From long to long long all the way
* Change more types to (hopefully) make the compiler happy
* Change many more types to size_t
* Change even more types to size_t
* Minor type changes
the code, but it helps visual studio use less RAM when building the example,
and might make appveyor not crash. It's also a
slightly cleaner way to write the code anyway.
* Exposed jitter_image in Python and added an example
* Return Numpy array directly
* Require numpy during setup
* Added install of Numpy before builds
* Changed pip install for user only due to security issues.
* Removed malloc
* Made presence of Numpy during compile optional.
* Conflict
* Refactored get_face_chip/get_face_chips to use Numpy as well.
* Add example of semantic segmentation using the PASCAL VOC2012 dataset
* Add note about Debug Information Format when using MSVC
* Make the upsampling layers residual as well
* Fix declaration order
* Use a wider net
* trainer.set_iterations_without_progress_threshold(5000); // (was 20000)
* Add residual_up
* Process entire directories of images (just easier to use)
* Simplify network structure so that builds finish even on Visual Studio (faster, or at all)
* Remove the training example from CMakeLists, because it's too much for the 32-bit MSVC++ compiler to handle
* Remove the probably-now-unnecessary set_dnn_prefer_smallest_algorithms call
* Review fix: remove the batch normalization layer from right before the loss
* Review fix: point out that only the Visual C++ compiler has problems.
Also expand the instructions how to run MSBuild.exe to circumvent the problems.
* Review fix: use dlib::match_endings
* Review fix: use dlib::join_rows. Also add some comments, and instructions where to download the pre-trained net from.
* Review fix: make formatting comply with dlib style conventions.
* Review fix: output training parameters.
* Review fix: remove #ifndef __INTELLISENSE__
* Review fix: use std::string instead of char*
* Review fix: update interpolation_abstract.h to say that extract_image_chips can now take the interpolation method as a parameter
* Fix whitespace formatting
* Add more comments
* Fix finding image files for inference
* Resize inference test output to the size of the input; add clarifying remarks
* Resize net output even in calculate_accuracy
* After all crop the net output instead of resizing it by interpolation
* For clarity, add an empty line in the console output
dimensions in the same format as the mmod_options object (i.e. two lengths
measured in pixels). This should make defining random_cropping strategies that
are consistent with MMOD settings much more straightforward since you can just
take the mmod_options settings and give them to the random_cropper and it will
do the right thing.
